How to measure your process

Robert S. Kaplan once said ”If you can’t measure it, you can’t manage it” and when it comes to processes, he couldn’t be more precise.

Think of your process as a car engine. On our previous post (How to design an efficient process) you learned how to build that engine, congrats! It won’t matter much how great that engine is if it never leaves the auto-shop.

Running a process is much like running an engine. From the moment it’s installed in the car what matters is finding out whether it works fine.

Sticking to the car example, these are a few relevant data you should collect to analyse your engine:

Gas intake per mile

Horse power

Oil and water levels

Temperature

Total mileage

Noise level

This is how you control whether an engine is running fine. Picture yourself on the driver seat trying to drive a car that has the engine you’ve built. Now try to think of yourself blindfolded, without being able to hear whether the engine is running, not knowing how much gas you have left or the speed you’re travelling.

When managing a process you can use the same approach. There are a few variables you should measure to help measure your process and figure out your productivity and whether it needs improvements.

How to measure your process?

It takes a couple of months running a process and gathering data to get a starting point for analysing its productivity and efficiency. Only after that initial analysis you’ll be able to think of ways to make your process more competitive and better than before.

To make understanding your process easier, think of it as an equation:

Output variables (Y variables)

The entire process you’ve built serves to guide your team while executing a specific activity towards achieving a determined output. But how can you know whether you’re achieving that output without measuring?

Primarily, it’s essential to measure your process’ end result, its output. This (or these) output variables can be determined by profit, amount of units produced, amount of new accounts, amount of helpdesk tickets assisted, etc.

This information normally addresses three questions: how much you produced, what was the delivery quality and how much time did it take.

When evaluating a car engine’s efficiency, for example, you’ll analyse many variables: horse power, miles per gallon, how much time it takes for a car to go from 0 to 60mph (or 100km/h, this measures acceleration), among many others.

Process execution variables (X variables)

The Y variables are cool, they’re your process’ ultimate goal. The problem is that you can’t interfere directly in them because they’re the output – they’re just information about what happened on the process.

Only measuring your process’ end result would be like staring at the end of a production line: you can see the end result of the process but you can’t analyse the factors that affect that end result.

In order to guarantee that the end result meet your expectations (or to interfere in it) you need to manage all the elements that affect that result. It would be like having a transparent hood to watch how your engine is running.

If you manage the variables that interfere in your process you’ll be able to safely predict the output. When interfering with the X variables you’ll impact the Y result. If you control the variables that affect your output closely the end result will become predictable.

The print shop case:

Picture yourself as the owner of a print shop. During a specific week one of your printers isn’t delivering as expected (Y variable). While trying to investigate the problem you’ll probably focus on observing the Y variables:

Was there a problem with the quality of the prints?

Was there a problem with the amount of prints produced?

Moving on with your investigation you’re likely to investigate what could have affected the prints (X variables):

Type of paper

Temperature of the paper

Humidity of the paper

Toner level

Position of the paper

Print operator’s work journey

Amount of time spent setting up the print

Managing salespeople:

It goes without saying that, when it comes to evaluating salespeople’ performances there’s a direct correlation between the quality of their work (X variables) and the amount of sales (Y variables).

Salespeople that prospect more customers sell more

Salespeope that have more meetings with customers sell more

Salespeople that send more proposals sell more

Salespeople that answer faster sell more.

There are a few sales industry indicators that clearly show that the way the salesperson manages a potential customer drastically influences his/her sales:

On average, 44% of all salespeople give up on a customer after a first follow-up attempt while 80% of successful sales demand at least four follow-ups. Do you identify the problem?

If a salesperson gets in touch with a potential customer within the first 5 minutes after he/she requested a contact he’ll be 9 times more likely to close the sale;

35% to 50% of the sales were assigned to sales that responded sooner.

If the amount of sales is the metric used to analyse a salesperson’s output (Y variable) it won’t matter how optimistic you are or the amount of candles you’ve lit asking for a miracle.

At the end of the month, if you need to make sure you have those sales to balance your finances you should really control all the variables than influence the end result (X variables).

In the case of the salesperson you’ll also need to measure the amount of inputs and evaluate if he/she is following each of the process’ steps according to the instructions.

Input variables (X variables):

Amount of leads

Quality of the leads

Process/execution variables:

Amount of calls made

Amount of meetings with customers

Amount of proposals sent

And if you dare to be a little more specific (and increase the complexity of the analysis):

Connection rate (number of effective contacts divided by the amount of leads)

Number of follow-ups by lead

Average response time after a budget is requested

Conversion rate (amount of sales divided by the number of meetings)

Average sales cycle (the amount of time between the first contact and the closing date)

Y variables:

Amount of sales ($) per month

Amount of new customers

Average ticket

Average profit margin (closing small deals is different than closing with a discount)

How can you determine the variables that interfere in your process’ execution (X) and represent your final productivity (Y)?

After you’ve designed your process (as described in how to design efficient processes), ask yourself the following questions:

Which variables can you use to measure your process’ inputs regarding the amount and quality of the resources?

Which are your process’ variables that can interfere in the end result?

How can you manage each variable’s impact? (remember the examples of the print shop and the salespeople)

What are the Y variables that can represent your process’ productivity?

What are the Y variables that can represent the quality of your output?

You can take note of these variables using the same structure you used to design your process, like the example below:

Check out the previous posts in this series:

Now that you know all about designing, managing and measuring your processes you should decide on a tool to help you do all that. You won’t even need to go far to find it: Pipefy is exactly what you’re looking for!

Design, manage and measure your process with Pipefy!

Pipefy is an intuitive, simple to use, process management software. It allow businesses of all sizes to run the entire company’s processes in a single platform, ensuring efficiency and solid execution.